Determination of weight coefficients for additive fitness function of genetic algorithm
This work addresses a specific optimization challenge in search engine query generation, but it appears incremental as it builds on existing genetic algorithm frameworks without introducing major new paradigms.
The paper tackles the problem of analytically determining weight coefficients for a genetic algorithm's additive fitness function, which is used to form stable and effective query populations in a search engine for highly relevant results, and presents a methodology based on experimental data from a prior project to illustrate the fitness function's behavior with different weighting options.
The paper presents a solution for the problem of choosing a method for analytical determining of weight factors for a genetic algorithm additive fitness function. This algorithm is the basis for an evolutionary process, which forms a stable and effective query population in a search engine to obtain highly relevant results. The paper gives a formal description of an algorithm fitness function, which is a weighted sum of three heterogeneous criteria. The selected methods for analytical determining of weight factors are described in detail. It is noted that expert assessment methods are impossible to use. The authors present a research methodology using the experimental results from earlier in the discussed project "Data Warehouse Support on the Base Intellectual Web Crawler and Evolutionary Model for Target Information Selection". There is a description of an initial dataset with data ranges for calculating weights. The calculation order is illustrated by examples. The research results in graphical form demonstrate the fitness function behavior during the genetic algorithm operation using various weighting options.